Libraries and settings

1. Import and format carbon chemestry data

Notes: * duplicated labels LK and 21LK; WS and WS cruise

Add seasonality

Winter: Dec 20-March 19
Spring: March 20-June 18
Summer: June 19-Sep 20
Fall: Sep 21-Dec 19
Wet: May - October
Dry: November - April

Subset only FK data

This removes samples from Gulf of Mexico
Add reef zone: “Inshore”, “Mid channel”, “Offshore”, “Oceanic”
Add reef subregion: “BB”, “UK”, “MK”, “LK”

 #Get only FLK data#
  Metadata<-read.csv("Data/FLKLocations.csv", header = T)
  
  FLK.data<-join(CC.data, Metadata, 
                 type = "left", by="SiteID")
  str(FLK.data)

## 'data.frame':    1977 obs. of  84 variables:
##  $ CTDID                  : chr  "WS10067_1" "WS10067_2" "WS10067_3" "WS10067_4" ...
##  $ Region                 : chr  "FL" "FL" "FL" "FL" ...
##  $ Year                   : num  2010 2010 2010 2010 2010 2010 2010 2010 2010 2010 ...
##  $ Mission                : chr  "WaltonSmith" "WaltonSmith" "WaltonSmith" "WaltonSmith" ...
##  $ Location               : chr  "Upper_Keys" "Upper_Keys" "Upper_Keys" "Upper_Keys" ...
##  $ Latitude               : num  25.6 25.6 25.1 25.1 25.1 ...
##  $ Longitude              : num  -80.1 -80.1 -80.4 -80.4 -80.3 ...
##  $ UTCDate                : Date, format: "10-03-08" "10-03-08" ...
##  $ UTCTime                : chr  "12:26:00 PM" "01:05:00 PM" "05:17:00 PM" "05:42:00 PM" ...
##  $ Sample_Depth_m         : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ DIC_umol_kg            : num  2073 2068 2187 2112 2081 ...
##  $ TA_umol_kg             : num  2424 2418 2558 2446 2428 ...
##  $ pH_measured            : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ pH_calculated          : num  8.16 8.14 8.19 8.15 8.15 ...
##  $ pCO2_uatm              : num  302 317 293 313 314 ...
##  $ Aragonite_Sat_W        : num  3.83 3.83 4.07 3.62 3.79 ...
##  $ Salinity_Bottle        : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ Conductivity_Sm        : logi  NA NA NA NA NA NA ...
##  $ Salinity_CTD           : num  36.5 36.5 36.7 36.7 36.6 ...
##  $ Temperature_C          : num  20.4 21.6 18.8 18.8 20.8 ...
##  $ Pressure_db            : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ Density_Sigmat         : logi  NA NA NA NA NA NA ...
##  $ SiteID                 : Factor w/ 112 levels "","1","10","11",..: 2 29 37 46 47 68 69 80 83 85 ...
##  $ Survey_design          : chr  "Permanent" "Permanent" "Permanent" "Permanent" ...
##  $ Sample_frequency       : chr  "Single" "Single" "Single" "Single" ...
##  $ ncrmp_flag             : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ bottle_id              : int  10021 10022 10023 10024 10025 10026 10027 10028 10029 10030 ...
##  $ source_code            : chr  "E" "E" "E" "E" ...
##  $ sample_num             : int  1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 ...
##  $ bottle_name            : chr  "1" "2" "3" "4" ...
##  $ deployment_id          : int  517 517 517 517 517 517 517 517 517 517 ...
##  $ project_code           : chr  "Walton Smith" "Walton Smith" "Walton Smith" "Walton Smith" ...
##  $ collection_id          : int  9221 9222 9223 9224 9225 9226 9227 9228 9229 9230 ...
##  $ collected_date         : chr  "03/08/10" "03/08/10" "03/08/10" "03/08/10" ...
##  $ collected_time         : chr  "12:26:00 PM" "01:05:00 PM" "05:17:00 PM" "05:42:00 PM" ...
##  $ collected_timezone     : chr  "12:00:00 AM" "12:00:00 AM" "12:00:00 AM" "12:00:00 AM" ...
##  $ density_id             : int  NA NA NA NA NA NA NA NA NA NA ...
##  $ dic_id                 : int  17166 17167 17168 17169 17170 17171 17172 17173 17174 17175 ...
##  $ calc_avg_dic           : num  2086 2081 2200 2125 2094 ...
##  $ dic_temp               : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ dic_machine            : chr  "SOMMA" "SOMMA" "SOMMA" "SOMMA" ...
##  $ dic_kg_flag            : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ calibration_dic_id     : int  590 590 590 590 590 590 590 590 590 590 ...
##  $ calc_avg_crm_init_dic  : num  2014 2014 2014 2014 2014 ...
##  $ crm_init_dic_temp      : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ calibration_dic_kg_flag: int  1 1 1 1 1 1 1 1 1 1 ...
##  $ dic_crm_id             : int  16 16 16 16 16 16 16 16 16 16 ...
##  $ dic_crm_batch          : int  85 85 85 85 85 85 85 85 85 85 ...
##  $ dic_crm_dic            : num  2000 2000 2000 2000 2000 ...
##  $ dic_crm_salinity       : num  33.3 33.3 33.3 33.3 33.3 ...
##  $ ta_id                  : int  9096 9097 9098 9099 9100 9101 9102 9103 9104 9105 ...
##  $ calc_avg_ta            : num  2455 2450 2590 2477 2459 ...
##  $ calc_avg_ta_temp       : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ ta_machine             : chr  "Langdonometer" "Langdonometer" "Langdonometer" "Langdonometer" ...
##  $ ta_kg_flag             : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ calibration_ta_id      : int  626 626 626 626 626 626 626 626 626 626 ...
##  $ calc_avg_crm_ta        : num  2215 2215 2215 2215 2215 ...
##  $ calc_avg_crm_ta_temp   : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ calibration_ta_kg_flag : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ ta_crm_id              : int  16 16 16 16 16 16 16 16 16 16 ...
##  $ ta_crm_batch           : int  85 85 85 85 85 85 85 85 85 85 ...
##  $ ta_crm_ta              : num  2184 2184 2184 2184 2184 ...
##  $ ta_crm_salinity        : num  33.3 33.3 33.3 33.3 33.3 ...
##  $ spec_ph_id             : int  NA NA NA NA NA NA NA NA NA NA ...
##  $ spec_ph_temp           : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ rm_factor              : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ seacarb_id             : int  4570 4571 4572 4573 4574 4575 4576 4577 4578 4579 ...
##  $ UTCDate_Time           : POSIXct, format: "2010-03-08 12:26:00" "2010-03-08 13:05:00" ...
##  $ datetime               : POSIXct, format: "2010-03-08 07:26:00" "2010-03-08 08:05:00" ...
##  $ ESTDate                : Date, format: "2010-03-08" "2010-03-08" ...
##  $ ESTTime                : chr  "07:26:00" "08:05:00" "12:17:00" "12:42:00" ...
##  $ Month                  : Factor w/ 12 levels "Apr","May","Jun",..: 12 12 12 12 12 12 12 12 12 12 ...
##  $ Months                 : Factor w/ 12 levels "January","February",..: 3 3 3 3 3 3 3 3 3 3 ...
##  $ MY                     : chr  "2010-03" "2010-03" "2010-03" "2010-03" ...
##  $ MoY                    : num  3 3 3 3 3 3 3 3 3 3 ...
##  $ ToD                    : num  7.43 8.08 12.28 12.7 12.75 ...
##  $ Season                 : chr  "Winter" "Winter" "Winter" "Winter" ...
##  $ Precipitation          : chr  "Dry" "Dry" "Dry" "Dry" ...
##  $ dec.lon                : num  -80.1 -80.1 -80.4 -80.4 -80.3 ...
##  $ dec.lat                : num  25.6 25.6 25.1 25.1 25.1 ...
##  $ Zone                   : chr  "Inshore" "Offshore" "Inshore" "Inshore" ...
##  $ Sub_region             : chr  "BB" "BB" "UK" "UK" ...
##  $ Reference              : chr  "BB_1" "BB_1" "UK_3" "UK_3" ...
##  $ Transect               : chr  "T1" "T1" "T1" "T1" ...

  FLK.data<-subset(FLK.data, FLK.data$Sub_region!="NA")
  
  FLK.data$Sub_region<-factor(FLK.data$Sub_region, levels = c(
                        "BB", "UK", "MK", "LK"))

  FLK.data$Zone<-factor(FLK.data$Zone, levels = c(
                        "Inshore", "Mid channel", 
                        "Offshore", "Oceanic"))

Label known extreme events to check outliers

# Label and filter extreme events
  FLK.data$Extreme<-"Normal"
  FLK.data$Extreme[FLK.data$MY=="2010-03"] <-"ColdMortality"
  FLK.data$Extreme[FLK.data$MY=="2010-08"] <-"Waves/Overcast"
  FLK.data$Extreme[FLK.data$MY=="2011-10"] <-"LowSalinty"
  FLK.data$Extreme[FLK.data$MY=="2011-08"] <-"HighOmega_NoReason"
  FLK.data$Extreme[FLK.data$MY=="2019-07"] <-"Low_pH"
  FLK.data$Extreme[FLK.data$MY=="2019-09"] <-"Low_pH"
  FLK.data$Extreme[FLK.data$MY=="2019-11"] <-"Low_pH"
  FLK.data$Extreme<-as.factor(FLK.data$Extreme)

2. Check data available

2.1 Locations (all samples)

Figure: Map of the sites where individual samples were collected

Notes: * Check location of individual SiteIDs

2.2 Average GPS point location

# Coordinates<-select(FLK.data, "CTDID", "Latitude", "Longitude", "SiteID")
# Coordinates<-join(Coordinates, WS.GPS.Sites,
#                  type = "left", by="SiteID")
# Coordinates$Lat_diff<-abs(Coordinates$Latitude-Coordinates$Lat)
# Coordinates$Lon_diff<-abs(Coordinates$Longitude-Coordinates$Lon)
# Check.coordinates1<- Coordinates[(Coordinates$Lat_diff>0.02), ]
# Check.coordinates2<- Coordinates[(Coordinates$Lon_diff>0.02), ]
# Check.coordinates<- rbind(Check.coordinates1, Check.coordinates2)
# Check.coordinates<-unique(Check.coordinates)
#write.csv(Check.coordinates, "FLK_results/2_CheckGPS_points_v3.csv")

Figure: Permanent location of WS stations

2.3 Number of samples

By zone and location

Sub_region	Inshore	Mid channel	Offshore	Oceanic
BB	84	76	83
UK	107	71	111	37
MK	129	129	130	83
LK	138	138	171	87

By year and sub_region

Samples collected each year
Sub_region	2010	2011	2012	2014	2015	2016	2017	2018	2019	2020	2021
BB	4	14	3	6	30	36	30	35	24	24	37
UK	7	19	3	8	45	51	42	45	30	31	45
MK	15	51	9	11	55	66	55	56	44	44	65
LK	18	61	9	11	58	72	60	76	53	46	70

By climatological season and region

Samples collected in each seson
Sub_region	Fall	Spring	Summer	Winter
LK	149	133	129	123
MK	133	120	121	97
UK	88	82	84	72
BB	68	60	63	52

By Month-year

Samples collected by year-month
2010	2011	2012	2014	2015	2016	2017	2018	2019	2020	2021	Months
					38	38	19	6	35		January
	16	22								38	February
24		2			38	35	33				March
	24			38			16	35		37	April
					38		16			28	May
20	24			38		38	35			8	June
				37	38	18		36		25	July
	28					20	38		35	10	August
				38	38			36			September
	28					38	18		38	36	October
				37	35			38			November
	25		36				37		37	35	December

By Month-region

Samples collected by month-location
Sub_region	Zone	Apr	May	Jun	Jul	Aug	Sep	Oct	Nov	Dec	Jan	Feb	Mar
BB	Inshore	9	4	8	10	5	6	8	6	10	7	4	7
BB	Mid channel	9	4	5	10	5	6	7	6	8	7	3	6
BB	Offshore	9	4	8	10	5	6	8	6	9	7	4	7
UK	Inshore	13	6	8	13	8	7	9	9	11	10	4	9
UK	Mid channel	6	3	9	9	3	6	6	5	8	7	2	7
UK	Offshore	11	5	12	14	6	9	11	8	11	12	3	9
UK	Oceanic	3	1	5	4	2	3	4	2	4	3	2	4
MK	Inshore	12	8	15	13	11	9	13	9	14	10	6	9
MK	Mid channel	12	8	13	13	11	9	13	9	14	9	7	11
MK	Offshore	13	7	13	13	11	9	13	9	15	10	6	11
MK	Oceanic	7	4	8	8	8	6	8	6	10	6	5	7
LK	Inshore	12	7	16	9	15	9	15	9	15	11	8	12
LK	Mid channel	12	7	15	10	13	9	15	9	15	12	9	12
LK	Offshore	14	10	18	13	18	12	19	12	17	18	7	13
LK	Oceanic	8	4	10	5	10	6	9	5	9	7	6	8

Per station during all sampling time

Samples collected in SiteIDs
SiteID	2010	2011	2012	2014	2015	2016	2017	2018	2019	2020	2021
1	2	6	1	1	5	6	5	6	4	4	6
10	2	5	1	1	5	6	5	5	4	4	5
11	2	4	1	1	5	6	5	5	4	4	6
12	1	5	1	1	5	6	5	6	4	4	6
13	1	4	1	1	5	6	5	4	4	4	6
14	1	3	1	1	5	6	5	5	4	4	6
16	2	6	1	1	5	6	5	6	4	4	5
17	2	6	1	1	5	6	5	7	4	4	5
18	2	6	1	1	5	6	5	7	4	4	6
19	2	6		1	5	6	5	6	4	4	6
20	2	5	1	1	5	6	5	6	4	4	6
21.5	2	6	1	1	4	6	5	5	4	3	6
21LK	1	5		1	5	6	5	5	4	4	6
22	1	3	1	1	5	6	5	7	5	4	6
22.5	1	6	1	1	4	6	5	6	5	3	6
23	1	6	1	1	5	6	5	7	5	4	6
24	2	6	1	1	5	6	5	7	5	4	6
3	2	6	1	1	5	6	5	6	4	4	6
4	1	4		1	5	6	5	4	2	3	4
5	1	4	1	1	5	6	5	4	2	3	4
5.5	2	2	1	1	5	5	4	4	3	3	4
6	2	4		1	5	5	4	4	3	3	5
6.5	1	5	1	1	5	5	4	4	3	3	5
7	2	6		1	5	6	5	6	4	4	6
8	2	5	1	1	5	6	5	6	4	4	6
9	2	6		1	5	6	5	6	4	4	6
9.5	2	6	1	1	5	6	5	4	4	4	6
15		4	1	1	5	6	5	5	4	4	6
15.5		3	1	1	5	6	5	4	4	4	6
2		2	1	1	5	6	5	6	4	4	6
EK_IN				1	5	6	5	6	4	4	7
EK_MID				1	5	6	5	5	4	4	6
EK_OFF				1	5	6	5	6	4	4	6
UK_IN				1	5	6	5	6	4	4	5
UK_MID				1	5	6	5	6	4	4	6
UK_OFF				1	5	6	5	6	4	4	6
MR					5	6	5	7	5	4	6
WS					5	6	5	7	5	4	6

3. TA and DIC salinity normalization

Salinity standarizations

Use salinity Bottle first, if not available, salinity CTD
Calculate TA intercepts

# TA_coef <- FLK.data %>% 
#                 group_by(Region) %>%
#                 do({model = lm(TA_umol_kg~BestSalinity, data=.)  # model
#                     data.frame(tidy(model), # get coefficient info
#                     glance(model))})        # get model info
    
 TA_coef <- FLK.data[FLK.data$Zone=="Oceanic",] %>% 
                 group_by(Region) %>%
                 do({model = lm(TA_umol_kg~BestSalinity, data=.)  # model
                     data.frame(tidy(model), # get coefficient info
                     glance(model))})        # get model info

Calculate DIC intercepts

    DIC_coef <- FLK.data[FLK.data$Zone=="Oceanic",] %>% 
                  group_by(Region) %>%
                  do({model = lm(DIC_umol_kg~BestSalinity, data=.)  
                  # create model
                  data.frame(tidy(model), # get coefficient info
                  glance(model))})        # get model info

    # DIC_coef <- FLK.data %>% 
    #               group_by(Region) %>%
    #               do({model = lm(DIC_umol_kg~BestSalinity, data=.)  
    #               # create model
    #               data.frame(tidy(model), # get coefficient info
    #               glance(model))})        # get model info

Normalize with Friss equation

# 4. Normalize the data
  TA_TO<-subset(TA_coef, term=="(Intercept)")
  TA_TO<-as.data.frame(select(TA_TO, c(Region, estimate)))
  names(TA_TO)[2] <- "TA_0"
  
  DIC_TO<-subset(DIC_coef, term=="(Intercept)")
  DIC_TO<-as.data.frame(select(DIC_TO, c(Region, estimate)))
  names(DIC_TO)[2] <- "DIC_0"
    
  Intercepts<-join(TA_TO, DIC_TO, by="Region", type="full")
  FLK.data<-join(FLK.data, Intercepts, by="Region", type="left")

FLK.data$nTA<-(
    (FLK.data$TA_umol_kg-FLK.data$TA_0)/
    FLK.data$BestSalinity)*MeanSalinity+FLK.data$TA_0

  FLK.data$nDIC<-(
    (FLK.data$DIC_umol_kg-FLK.data$DIC_0)/
    FLK.data$BestSalinity)*MeanSalinity+FLK.data$DIC_0
  
#write.csv(FLK.data, "FLK_results/FLK.data_normalized.csv", row.names = F)

TA parameters to nTA

kable(as.data.frame(TA_coef[,1:8], format = "markdown"), 
      caption = "TA parameters for normalization", digits = 3)

TA parameters for normalization
Region	term	estimate	std.error	statistic	p.value	r.squared	adj.r.squared
FL	(Intercept)	2111.560	57.288	36.859	0	0.099	0.094
FL	BestSalinity	7.515	1.592	4.719	0	0.099	0.094

#write.csv(TA_coef, "TA_coef.csv")

# 5. Chech normalization?
#nTA_Sal<- ggplot(FLK.data[FLK.data$Zone=="Oceanic",] ) + theme_bw() +
  
nTA_Sal<- ggplot(FLK.data) + MyTheme+
facet_grid(~Zone)+
 
  geom_point(aes (BestSalinity, TA_umol_kg, fill=Season), alpha=0.5, shape=21)+
  geom_smooth(aes(BestSalinity, TA_umol_kg), linetype=2, method = "lm", color="black")+
    
  geom_point(aes (BestSalinity, nTA, fill=Season), alpha=0.5, shape=24)+
  geom_smooth(aes(BestSalinity, nTA), linetype=1, method = "lm")
    
  #geom_point(aes (BestSalinity, n35TA, fill=Season), alpha=0.5, shape=25)+
  #geom_smooth(aes(BestSalinity, n35TA), linetype=3, method = "lm")+  

#nTA_Sal
#ggsave(file="Outputs/Figure_1_Experiment_design.svg", plot=Design, dpi = 300, width=6, height=4)

DIC parameters to nDIC

kable(as.data.frame(DIC_coef[,1:8], format = "markdown"), 
      caption = " DIC parameters for normalization", digits = 3)

DIC parameters for normalization
Region	term	estimate	std.error	statistic	p.value	r.squared	adj.r.squared
FL	(Intercept)	1786.677	115.049	15.530	0.000	0.025	0.02
FL	BestSalinity	7.244	3.199	2.264	0.025	0.025	0.02

#write.csv(DIC_coef, "DIC_coef.csv")

nDIC_Sal<- ggplot(FLK.data) + MyTheme+
  facet_grid(~Zone)+
#nDIC_Sal<-ggplot(FLK.data[FLK.data$Zone=="Oceanic",] ) + theme_bw() +
  geom_point(aes (BestSalinity, DIC_umol_kg, fill=factor(Season)),
             alpha=0.5, shape=21)+
  geom_smooth(aes(BestSalinity, DIC_umol_kg),
              linetype=2, method = "lm", color="black")+
    
  geom_point(aes (BestSalinity, nDIC, fill=factor(Season)), alpha=0.5, shape=24)+
  geom_smooth(aes(BestSalinity, nDIC), linetype=1, method = "lm")
#nDIC_Sal

#ggsave(file="Outputs/Figure_1_Experiment_design.svg", plot=Design, dpi = 300, width=6, height=4)

FLK_parameters<-ggarrange(nTA_Sal, nDIC_Sal,
          #labels = c("DIC Friss regresion", "TA Friss regresion"),
          ncol = 1, nrow = 2)

FLK_parameters

Figure 1: Overview of linear regressions of total alkalinity (TA, µmol kg−1) and dissolved inorganic carbon (DIC, µmol kg−1) as a function of salinity + Friss normalized values.

Original data: Black line and circles
Friis normalization: blue line and triangles
Highest and lowest salinities are most likely wrong

Bias of hour of the day per sample (EST)

All_ToD<- ggplot(FLK.data) + 
  
  theme_bw() +
  scale_y_continuous(limits = c(0, 24),
                      expand = c(0.01, 0.01),
                      breaks = seq(0, 24, 2),
                      name="Time of the day")+
  
  theme(legend.position="bottom",
          plot.background=element_blank(),
          panel.grid = element_blank(),
          legend.box.background = element_rect(),
          panel.background =element_rect(fill = NA, 
                                         color = "black")
          )

nTA_dat<-All_ToD +geom_point(aes(nTA, ToD, colour=Sub_region))#+
  #geom_smooth(aes(nDIC, ToD, colour=Sub_region), se=F)
ggMarginal(nTA_dat, groupColour = TRUE)

nDIC_dat<-All_ToD +geom_point(aes(nDIC, ToD, colour=Sub_region))#+
  #geom_smooth(aes(nDIC, ToD, colour=Sub_region), se=F)
ggMarginal(nDIC_dat, groupColour = TRUE)

omega_dat<-All_ToD +geom_point(aes(Aragonite_Sat_W, ToD, colour=Sub_region))#+
  #geom_smooth(aes(ToD, Aragonite_Sat_W, colour=Sub_region), se=F)
ggMarginal(omega_dat, groupColour = TRUE)

4. nTA and nDIC relation

4.1 Region (all FLK samples)

nTA_nDIC<- ggplot(FLK.data, aes (nDIC, nTA)) + 
  
  scale_y_continuous(#limits = c(1700,2900),
                     #expand = c(0, 0),
                     #breaks = seq(1800, 2800, 100),
                    name="nTA") +
  scale_x_continuous(# limits = c(1600, 2600),
                     # expand = c(0, 0),
                     # breaks = seq(1500,2600,100),
                      name="nDIC")+
MyTheme

#TA_DIC+ 
 #geom_point(aes (fill=Region, shape=Season), alpha=0.5)+
 #scale_shape_manual(values=c(21, 22, 23, 24))

#ggsave(file="Outputs/Figure_1_Experiment_design.svg", plot=Design, dpi = 300, width=6, height=4)

nTA_nDIC_sea<-nTA_nDIC +
   scale_shape_manual(values=c(21,21,24, 24))+
  geom_point(aes (fill=Season, shape=Zone), alpha=0.4)+
  geom_smooth(method = "lm", colour="black")

nTA_nDIC_pre<-nTA_nDIC +
  scale_shape_manual(values=c(21,21,24, 24))+
  geom_point(aes (fill=Precipitation,  shape=Zone), alpha=0.4)+
  geom_smooth(method = "lm", colour="black")

nTA_nDIC_parameters<-ggarrange(nTA_nDIC_sea, nTA_nDIC_pre,
          #labels = c("DIC Friss regresion", "TA Friss regresion"),
          ncol = 2, nrow = 1)

nTA_nDIC_parameters

Figure 2: Linear regressions of total alkalinity (nTA) as a function of disolved inorganic carbon (nDIC) for the Florida Keys .

Does this mean anything without offshore / inshore references?

 # Individual LR for each region
    TA_DIC_lm <- FLK.data %>%
      group_by(Region) %>%
      do(mod = lm(nTA ~ nDIC, data = .))

    TA_DIC_coef <- FLK.data %>% 
                    group_by(Region) %>%
                    do({model = lm(nTA~nDIC, data=.)  
                    # create model
                    data.frame(tidy(model), # get coefficient info
                    glance(model))})        # get model info

    kable(as.data.frame(TA_DIC_coef[,1:8], format = "markdown"),
          caption = "nTA vs nDIC equations", digits = 3)

nTA vs nDIC equations
Region	term	estimate	std.error	statistic	p.value	r.squared	adj.r.squared
FL	(Intercept)	801.063	23.578	33.975	0	0.74	0.739
FL	nDIC	0.768	0.012	66.386	0	0.74	0.739

4.2 By zone sub_region

nTA_nDIC_sea_zone<-nTA_nDIC_sea + facet_grid(Zone~Sub_region)
nTA_nDIC_sea_zone

nTA_nDIC_pre_zone<-nTA_nDIC_pre +  facet_grid(Zone~Sub_region)
nTA_nDIC_pre_zone

 # Individual LR for each Zone and Sub_region
    TA_DIC_lm_zone <- FLK.data %>%
      group_by(Zone, Sub_region) %>%
      do(mod = lm(nTA ~ nDIC, data = .))

    TA_DIC_coef_zone <- FLK.data %>% 
                    group_by(Zone, Sub_region) %>%
                    do({model = lm(nTA~nDIC, data=.)  
                    # create model
                    data.frame(tidy(model), # get coefficient info
                    glance(model))})        # get model info

    kable(as.data.frame(TA_DIC_coef_zone[,1:8], format = "markdown"),
          caption = "nTA vs nDIC equations", digits = 3)

nTA vs nDIC equations
Zone	Sub_region	term	estimate	std.error	statistic	p.value	r.squared
Inshore	BB	(Intercept)	1059.443	82.755	12.802	0.000	0.755
Inshore	BB	nDIC	0.639	0.040	15.903	0.000	0.755
Inshore	UK	(Intercept)	1194.064	61.454	19.430	0.000	0.782
Inshore	UK	nDIC	0.581	0.030	19.326	0.000	0.782
Inshore	MK	(Intercept)	638.224	67.190	9.499	0.000	0.837
Inshore	MK	nDIC	0.849	0.033	25.456	0.000	0.837
Inshore	LK	(Intercept)	611.540	77.406	7.900	0.000	0.782
Inshore	LK	nDIC	0.845	0.039	21.819	0.000	0.782
Mid channel	BB	(Intercept)	1463.125	99.432	14.715	0.000	0.534
Mid channel	BB	nDIC	0.446	0.048	9.203	0.000	0.534
Mid channel	UK	(Intercept)	1710.133	105.684	16.182	0.000	0.369
Mid channel	UK	nDIC	0.328	0.052	6.351	0.000	0.369
Mid channel	MK	(Intercept)	776.598	86.379	8.991	0.000	0.728
Mid channel	MK	nDIC	0.782	0.043	18.353	0.000	0.728
Mid channel	LK	(Intercept)	652.923	79.371	8.226	0.000	0.774
Mid channel	LK	nDIC	0.837	0.039	21.448	0.000	0.774
Offshore	BB	(Intercept)	1468.342	140.296	10.466	0.000	0.343
Offshore	BB	nDIC	0.445	0.068	6.503	0.000	0.343
Offshore	UK	(Intercept)	1585.295	99.774	15.889	0.000	0.372
Offshore	UK	nDIC	0.388	0.049	7.963	0.000	0.372
Offshore	MK	(Intercept)	1101.649	99.094	11.117	0.000	0.565
Offshore	MK	nDIC	0.624	0.049	12.848	0.000	0.565
Offshore	LK	(Intercept)	823.857	91.742	8.980	0.000	0.636
Offshore	LK	nDIC	0.758	0.045	16.930	0.000	0.636
Oceanic	UK	(Intercept)	1947.839	176.508	11.035	0.000	0.151
Oceanic	UK	nDIC	0.213	0.086	2.462	0.019	0.151
Oceanic	MK	(Intercept)	2232.810	101.778	21.938	0.000	0.026
Oceanic	MK	nDIC	0.073	0.050	1.461	0.148	0.026
Oceanic	LK	(Intercept)	1900.529	122.199	15.553	0.000	0.158
Oceanic	LK	nDIC	0.235	0.060	3.945	0.000	0.158

Notes:

Higher slopes in TA vs. DIC
- -> a higher NCC:NCP
- -> calcifying (e.g., corals, sediments, and calcifying algae) relative to non-calcifying communities (e.g., fleshy algae)
Intermediate slopes for mixed communities of calcifiers and non-calcifiers (Anthony et al., 2013; Page et al., 2016; Lantz et al., 2017).

4.3 nTA nDIC outliers

nTA_nDIC_Extreme<- ggplot() + 
    scale_y_continuous(#limits = c(1700,2900),
                     #expand = c(0, 0),
                     #breaks = seq(1800, 2800, 100),
                    name="nTA") +
  scale_x_continuous(# limits = c(1600, 2600),
                     # expand = c(0, 0),
                     # breaks = seq(1500,2600,100),
                      name="nDIC")+
  MyTheme +  facet_grid(Zone~Sub_region)+
  geom_point(data=FLK.data, aes (nDIC, nTA, fill=Extreme), shape=21, alpha=0.8)+
  geom_smooth(data=FLK.data[FLK.data$Extreme=="Normal", ], 
              aes (nDIC, nTA), method = "lm", colour="black")
nTA_nDIC_Extreme

 # Individual LR for each Zone and Sub_region
    
    TA_DIC_coef_zone_noExtremes <- FLK.data[FLK.data$Extreme=="Normal", ] %>% 
                    group_by(Zone, Sub_region) %>%
                    do({model = lm(nTA~nDIC, data=.)  
                    # create model
                    data.frame(tidy(model), # get coefficient info
                    glance(model))})        # get model info

    kable(as.data.frame(TA_DIC_coef_zone_noExtremes[,1:8], format = "markdown"),
          caption = "nTA vs nDIC equations without extreme events", digits = 3)

nTA vs nDIC equations without extreme events
Zone	Sub_region	term	estimate	std.error	statistic	p.value	r.squared
Inshore	BB	(Intercept)	989.867	84.382	11.731	0.000	0.786
Inshore	BB	nDIC	0.674	0.041	16.393	0.000	0.786
Inshore	UK	(Intercept)	1169.670	60.712	19.266	0.000	0.808
Inshore	UK	nDIC	0.593	0.030	19.921	0.000	0.808
Inshore	MK	(Intercept)	645.885	65.423	9.872	0.000	0.860
Inshore	MK	nDIC	0.846	0.033	25.974	0.000	0.860
Inshore	LK	(Intercept)	592.689	83.195	7.124	0.000	0.785
Inshore	LK	nDIC	0.855	0.042	20.487	0.000	0.785
Mid channel	BB	(Intercept)	1246.972	109.364	11.402	0.000	0.614
Mid channel	BB	nDIC	0.552	0.053	10.332	0.000	0.614
Mid channel	UK	(Intercept)	1553.603	124.023	12.527	0.000	0.418
Mid channel	UK	nDIC	0.405	0.061	6.667	0.000	0.418
Mid channel	MK	(Intercept)	698.676	75.025	9.313	0.000	0.817
Mid channel	MK	nDIC	0.822	0.037	22.169	0.000	0.817
Mid channel	LK	(Intercept)	735.616	78.104	9.418	0.000	0.785
Mid channel	LK	nDIC	0.797	0.038	20.698	0.000	0.785
Offshore	BB	(Intercept)	1160.019	172.795	6.713	0.000	0.409
Offshore	BB	nDIC	0.595	0.084	7.052	0.000	0.409
Offshore	UK	(Intercept)	1265.409	108.426	11.671	0.000	0.524
Offshore	UK	nDIC	0.545	0.053	10.270	0.000	0.524
Offshore	MK	(Intercept)	799.662	78.851	10.141	0.000	0.782
Offshore	MK	nDIC	0.772	0.039	19.958	0.000	0.782
Offshore	LK	(Intercept)	1223.653	85.134	14.373	0.000	0.559
Offshore	LK	nDIC	0.563	0.042	13.519	0.000	0.559
Oceanic	UK	(Intercept)	1541.985	200.853	7.677	0.000	0.375
Oceanic	UK	nDIC	0.411	0.098	4.172	0.000	0.375
Oceanic	MK	(Intercept)	2250.535	110.943	20.285	0.000	0.019
Oceanic	MK	nDIC	0.063	0.054	1.167	0.247	0.019
Oceanic	LK	(Intercept)	1560.316	128.211	12.170	0.000	0.365
Oceanic	LK	nDIC	0.401	0.063	6.393	0.000	0.365

Notes: * Offshore high nTA and nDIC during march 2012 cold mortality event - keep or remove?

5. Basic stats summary and visual

5.1 Temperature

FLK.data$Date<-FLK.data$ESTDate

Season stats

Temperature by season
Season	Zone	Tmin	Tmax	Tmean	Tsd
Fall	Inshore	20.01500	30.02900	25.59673	2.5421481
Fall	Mid channel	21.98100	29.85110	26.62821	2.0493446
Fall	Offshore	24.87700	29.97035	27.20738	1.5786848
Fall	Oceanic	24.90300	30.01320	27.39854	1.5239508
Spring	Inshore	22.23800	32.79000	27.85472	1.9082703
Spring	Mid channel	24.46990	31.26000	27.59277	1.6558646
Spring	Offshore	24.72433	31.12400	27.20822	1.4543694
Spring	Oceanic	25.07167	30.70000	27.28511	1.4048604
Summer	Inshore	26.85130	33.41420	30.94559	1.3603574
Summer	Mid channel	27.69640	33.54900	30.66481	1.0530289
Summer	Offshore	28.57000	32.47400	30.35070	0.7031212
Summer	Oceanic	29.36600	31.42030	30.27019	0.4979293
Winter	Inshore	17.50500	26.65400	22.66720	2.3468100
Winter	Mid channel	17.91567	26.79900	23.63902	1.8934145
Winter	Offshore	19.19967	27.21600	24.27852	1.5846746
Winter	Oceanic	21.34400	27.30000	24.58368	1.6286770

Overview

Monthly

5.2 Salinity

Season stats

Salinity by season
Season	Zone	Sal_min	Sal_max	Sal_mean	Sal_sd
Fall	Inshore	30.45233	38.11319	35.35753	1.2447767
Fall	Mid channel	31.88800	37.26015	35.62053	0.8406938
Fall	Offshore	33.47100	36.86900	35.71652	0.6347185
Fall	Oceanic	33.90255	37.18100	35.78349	0.5204920
Spring	Inshore	35.08521	37.73900	36.67620	0.5579413
Spring	Mid channel	35.45246	37.23920	36.44229	0.3454641
Spring	Offshore	34.82339	36.73600	36.33263	0.2553492
Spring	Oceanic	35.75627	36.66305	36.31844	0.2095783
Summer	Inshore	32.05500	41.03199	36.17870	1.1432207
Summer	Mid channel	33.94800	36.86176	35.86870	0.6988476
Summer	Offshore	33.84100	36.55435	35.72740	0.6229552
Summer	Oceanic	32.73250	36.51863	35.56385	0.7789012
Winter	Inshore	31.23138	38.32563	35.98450	0.9184624
Winter	Mid channel	33.25478	36.77829	36.12218	0.5298124
Winter	Offshore	33.64836	36.62865	36.20181	0.4361290
Winter	Oceanic	35.47867	36.56613	36.23997	0.2947979

Overview

Monthly

Salinity issues

Salinity_low<-FLK.data[FLK.data$BestSalinity<30, ]
Salinity_high<-FLK.data[FLK.data$BestSalinity>40, ]
Salinity_Check<-rbind(Salinity_low, Salinity_high)
Salinity_Check<-Salinity_Check[!is.na(Salinity_Check$BestSalinity),]
Salinity_Check$Problem<-"salinity"
Salinity_Check

Salinity - Temperatue correlations and outliers

5.3 Aragonite

Season stats

Omega by season
Season	Zone	Amin	Amax	Amean	Asd
Fall	Inshore	2.265938	4.153585	3.237630	0.3377839
Fall	Mid channel	2.425071	4.194368	3.554209	0.2828936
Fall	Offshore	3.172859	4.472063	3.722611	0.2317087
Fall	Oceanic	3.261485	4.216995	3.779979	0.2274992
Spring	Inshore	2.804235	5.083392	4.139778	0.4495737
Spring	Mid channel	2.766048	4.711981	3.965309	0.3038914
Spring	Offshore	2.999385	4.251338	3.780495	0.1547279
Spring	Oceanic	1.998959	4.078478	3.764759	0.2894793
Summer	Inshore	1.835257	5.148959	3.621862	0.5640274
Summer	Mid channel	2.808160	4.918125	3.803552	0.3656937
Summer	Offshore	3.110143	5.133387	3.905004	0.3108872
Summer	Oceanic	3.553370	4.945832	4.016958	0.3165640
Winter	Inshore	2.401692	4.886632	3.626888	0.4247764
Winter	Mid channel	2.339249	4.649625	3.624081	0.3020022
Winter	Offshore	3.201501	4.312818	3.651986	0.1540584
Winter	Oceanic	2.491916	3.996918	3.676481	0.2192837

Overview

Monthly

Omega issues

Omega_low<-FLK.data[FLK.data$Aragonite_Sat_W<2.1, ]
Omega_low2<-FLK.data[FLK.data$Aragonite_Sat_W<3 & FLK.data$Zone=="Oceanic", ]
Omega_high<-FLK.data[FLK.data$Extreme=="HighOmega_NoReason", ]
Omega_Check<-rbind(Omega_low, Omega_low2, Omega_high)
Omega_Check<-Omega_Check[!is.na(Omega_Check$Aragonite_Sat_W),]
Omega_Check$Problem<-"omega"
Omega_Check

Omega - Temperature correlation and outliers

Omega - Salinity correlation and outliers

5.4 TA

Season stats

TA by season
Season	Zone	TAmin	TAmax	TAmean	TAsd
Fall	Inshore	2099.544	2609.396	2371.411	87.47261
Fall	Mid channel	2174.910	2479.733	2372.264	43.14992
Fall	Offshore	2245.810	2406.123	2376.256	20.65044
Fall	Oceanic	2353.810	2406.854	2379.771	10.49261
Spring	Inshore	2091.859	2398.144	2298.375	71.80582
Spring	Mid channel	2114.163	2406.498	2353.235	52.62884
Spring	Offshore	2262.590	2426.836	2376.541	21.89812
Spring	Oceanic	2339.600	2403.372	2382.003	12.16319
Summer	Inshore	1943.671	2494.752	2280.082	100.46661
Summer	Mid channel	2131.950	2411.730	2338.454	57.52611
Summer	Offshore	2293.290	2422.590	2369.555	25.84041
Summer	Oceanic	2343.852	2424.110	2382.153	16.92446
Winter	Inshore	2251.164	2781.489	2430.625	85.09654
Winter	Mid channel	2274.057	2678.780	2401.440	50.82598
Winter	Offshore	2352.434	2722.780	2389.688	36.50677
Winter	Oceanic	2354.310	2420.980	2383.905	15.82864

nTA by season
Season	Zone	TAmin	TAmax	TAmean	TAsd
Fall	Inshore	2100.062	2622.230	2375.332	88.87421
Fall	Mid channel	2183.076	2467.268	2374.259	41.58527
Fall	Offshore	2255.948	2426.871	2378.143	19.89908
Fall	Oceanic	2354.102	2413.657	2381.170	10.71712
Spring	Inshore	2092.177	2394.805	2295.050	71.14263
Spring	Mid channel	2114.175	2402.744	2350.306	52.12717
Spring	Offshore	2262.578	2426.413	2374.111	21.66102
Spring	Oceanic	2340.042	2399.080	2379.600	11.49296
Summer	Inshore	1945.264	2491.106	2280.584	101.13850
Summer	Mid channel	2131.472	2409.171	2339.601	58.30205
Summer	Offshore	2290.650	2423.974	2371.553	25.92975
Summer	Oceanic	2352.406	2430.996	2385.388	14.82703
Winter	Inshore	2251.407	2799.240	2431.781	89.97731
Winter	Mid channel	2287.465	2686.370	2400.489	51.34077
Winter	Offshore	2351.532	2719.498	2388.230	36.61700
Winter	Oceanic	2354.914	2416.243	2382.094	15.59514

Overview - outliers

Monthly

nTA issues

TA_low<-FLK.data[FLK.data$nTA<2000, ] # One sample (WS20231_26) low TA no DIC
TA_high<-FLK.data[FLK.data$nTA>2780, ] # One sample (WS17030_16) high
TA_Check<-rbind(TA_low, TA_high)
TA_Check<-TA_Check[!is.na(TA_Check$nTA),]
TA_Check$Problem<-"TA"
TA_Check

Omega- nTA scatterplot and outliers

5.5 DIC

Season stats

DIC by season
Season	Zone	DIC_min	DIC_max	DIC_mean	DIC_sd
Fall	Inshore	1834.164	2347.652	2088.697	84.08436
Fall	Mid channel	1940.415	2188.637	2060.520	40.73712
Fall	Offshore	1956.640	2113.588	2049.048	29.11836
Fall	Oceanic	1957.930	2118.330	2047.986	29.49927
Spring	Inshore	1724.374	2078.210	1923.199	72.07535
Spring	Mid channel	1793.125	2109.220	2000.008	54.45072
Spring	Offshore	1918.379	2118.600	2042.020	24.54888
Spring	Oceanic	2003.835	2182.680	2049.730	24.77367
Summer	Inshore	1704.388	2186.988	1970.661	94.51700
Summer	Mid channel	1783.686	2159.260	2009.134	59.21836
Summer	Offshore	1925.061	2108.781	2032.781	32.27661
Summer	Oceanic	1979.375	2102.520	2036.585	28.68785
Winter	Inshore	1943.358	2429.507	2106.100	87.95805
Winter	Mid channel	1991.463	2310.755	2076.757	53.87955
Winter	Offshore	2012.429	2339.885	2062.173	33.69393
Winter	Oceanic	2016.510	2158.478	2054.552	23.70995

nDIC by season
Season	Zone	nDIC_min	nDIC_max	nDIC_mean	nDIC_sd
Fall	Inshore	1832.114	2362.113	2094.227	85.92126
Fall	Mid channel	1955.011	2178.232	2063.105	39.13664
Fall	Offshore	1964.219	2115.344	2051.026	28.35117
Fall	Oceanic	1962.517	2130.570	2049.597	29.94949
Spring	Inshore	1723.884	2080.800	1921.052	71.80370
Spring	Mid channel	1793.154	2111.825	1997.540	54.32813
Spring	Offshore	1918.368	2117.514	2039.684	24.38300
Spring	Oceanic	2002.499	2183.448	2047.425	24.80804
Summer	Inshore	1710.799	2197.361	1971.612	96.78660
Summer	Mid channel	1783.699	2171.356	2010.607	61.57434
Summer	Offshore	1923.051	2114.592	2034.842	33.28861
Summer	Oceanic	1980.744	2103.706	2039.835	28.78964
Winter	Inshore	1938.599	2446.540	2107.336	92.33377
Winter	Mid channel	1988.309	2317.768	2076.023	55.15772
Winter	Offshore	2010.683	2336.915	2060.726	34.18601
Winter	Oceanic	2017.082	2158.698	2052.812	24.05237

Overview - outliers

Monthly

nDIC issues

Not filtering additional data becuase of nDIC

Omega - nDIC scatterplot and outliers

NOTE: Review Omega calculations for 2011 - Very high values for normal DIC and TA - including offshore and ocean samples

5.6 pH

Season stats

pH by season
Season	Zone	pH_min	pH_max	pH_mean	pH_sd
Fall	Inshore	7.840293	8.109975	7.988004	0.0618658
Fall	Mid channel	7.908322	8.092337	8.019632	0.0406883
Fall	Offshore	7.937204	8.157007	8.034243	0.0379669
Fall	Oceanic	7.949192	8.104629	8.038466	0.0386797
Spring	Inshore	7.875770	8.271259	8.099446	0.0769675
Spring	Mid channel	7.877835	8.221791	8.066494	0.0547752
Spring	Offshore	7.942578	8.100547	8.040308	0.0286746
Spring	Oceanic	7.730269	8.092541	8.033934	0.0495861
Summer	Inshore	7.672562	8.172603	7.966685	0.0765092
Summer	Mid channel	7.868227	8.125938	7.990184	0.0457906
Summer	Offshore	7.883689	8.147040	8.004235	0.0413498
Summer	Oceanic	7.958271	8.119377	8.020132	0.0414338
Winter	Inshore	7.925370	8.246944	8.087370	0.0623184
Winter	Mid channel	7.920679	8.230011	8.076427	0.0439480
Winter	Offshore	8.033234	8.183797	8.073114	0.0242499
Winter	Oceanic	7.902464	8.144928	8.071353	0.0348490

Overview - outliers

Monthly

pH issues

pH_low<-FLK.data[FLK.data$pH_calculated<7.95 & FLK.data$Zone=="Oceanic", ]
pH_low$Problem<-"pH(low_for_ocean)"

Omega - nDIC scatterplot and outliers

NOTE: Review Omega calculations for August 2011 - Very high values for normal DIC and TA - including offshore and ocean samples

6 Remove outliers

Remove<-rbind(Salinity_Check, Omega_Check, TA_Check, pH_low)
Remove <-Remove %>% 
        filter(!duplicated(CTDID))

#write.csv(Remove, "FLK_results/ValuesToRemove_V4.csv", row.names = F)

FLK.data_filtered<-FLK.data
#FLK.data_filtered<-FLK.data[!(FLK.data$CTDID %in% Remove$CTDID),]
FLK.data_filtered<-FLK.data_filtered%>% select (- (Survey_design: seacarb_id))
#str(FLK.data_filtered)
#write.csv(FLK.data_filtered, "FLK_results/FLK_filtered_ve3.csv", row.names = F)

7. Notes/ideas

Regional CC -> offshore data
Local carbonate chemistry:
- Seasonal (light), storms, upwelling, salinity (rain).
- Biological: coral/seagrass/macroalgae cover
- Biological + Seasonal: Photosynthesis, respiration.
- Physical: inlets, rivers, upwelling

Manzello (2012)

local carbonate chemistry: Variation on CO2, alkalinity, and salinity - Florida Reef Tract (FRT) data.
Measures total CO2 (TCO2) - colometrically and total alkalinity (TA) - gran tritration. Other parameters derived
Predominantly in inshore / upper FRT (higher than modeled).
H Productivity -> net uptake of total CO2 (TCO2) -> H aragonite *Offshore reefs: oceanic carbonate chemistry consistent with present day tropical surface ocean conditions.
Photosynthetic uptake of TCO2 by seagrasses/macroalgae in the inshore waters of the FRT. Acidification refugia (spatial * time)

Uthicke (2014)

GBR - 14 Inshore fringing reefs Measured: TA and DIC. Derived:pCO2, pH and aragonite
TA: 2069 - 2364 mmol kg2
DIC:1846 - 2099 mmol kg2
pCO2: 340 - 554 matm
- Higher wet seasons
- Inshore reefs above atmospheric values.
- Arg was not further reduced in the wet season (temp effects) *Thermodynamic effects and anthropogenic runoff (P, R and P/R ratios).
- Expected slope of linear regression bw DIC and TA in systems where calcification is dominating is ~ 2.0

Cyronak (2018)

Net community production; NCP - organic
net community calcification; NCC inorganic
23 coral reef locations across the globe - derived from TA and DIC
All reefs were net calcifying for the majority of observations (alkalinity depletion relative to offshore)
Occasional observations of net dissolution occurred at most locations
Influence of organic carbon fluxes on total changes in dissolved inorganic carbon (DIC) (i.e., NCP compared to the sum of NCP and NCC) = 32% to 88% -> biogeochemical differences between reefs.
Reefs with the largest relative percentage of NCP experienced the largest variability in seawater pH for a given change in DIC -> ability to elevate or suppress local pH relative to the open ocean.

Ian (2019)

Reefs and inlets were sampled
Reefs: Elevated pCO2 in warmer wet season
Inlets: more dynamic -> periodic pulses of acidified water -> more acidification than reefs.
- Negative correlation bw salinity and both TA, and DIC. Elevated TA and DIC in low salinity waters -> carbonate dissolution as a result of organic matter decomposition?.

Sub_region	2010	2011	2012	2014	2015	2016	2017	2018	2019	2020	2021
BB	4	14	3	6	30	36	30	35	24	24	37
UK	7	19	3	8	45	51	42	45	30	31	45
MK	15	51	9	11	55	66	55	56	44	44	65
LK	18	61	9	11	58	72	60	76	53	46	70

Sub_region	2010	2011	2012	2014	2015	2016	2017	2018	2019	2020	2021
BB	4	14	3	6	30	36	30	35	24	24	37
UK	7	19	3	8	45	51	42	45	30	31	45
MK	15	51	9	11	55	66	55	56	44	44	65
LK	18	61	9	11	58	72	60	76	53	46	70

Carbon Chemestry explore - New database

Ana Palacio-Castro

2023-09-21

Libraries and settings

1. Import and format carbon chemestry data

Add seasonality

Subset only FK data

Label known extreme events to check outliers

2. Check data available

2.1 Locations (all samples)

2.2 Average GPS point location

2.3 Number of samples

By zone and location

By year and sub_region

By climatological season and region

By Month-year

By Month-region

Per station during all sampling time

3. TA and DIC salinity normalization

Salinity standarizations

Bias of hour of the day per sample (EST)

4. nTA and nDIC relation

4.1 Region (all FLK samples)

4.2 By zone sub_region

4.3 nTA nDIC outliers

5. Basic stats summary and visual

5.1 Temperature

Season stats

Overview

Monthly

5.2 Salinity

Season stats

Overview

Monthly

Salinity issues

Salinity - Temperatue correlations and outliers

5.3 Aragonite

Season stats

Overview

Monthly

Omega issues

Omega - Temperature correlation and outliers

Omega - Salinity correlation and outliers

5.4 TA

Season stats

Overview - outliers

Monthly

nTA issues

Omega- nTA scatterplot and outliers

5.5 DIC

Season stats

Overview - outliers

Monthly

nDIC issues

Omega - nDIC scatterplot and outliers

5.6 pH

Season stats

Overview - outliers

Monthly

pH issues

Omega - nDIC scatterplot and outliers

6 Remove outliers

7. Notes/ideas

Manzello (2012)

Uthicke (2014)

Cyronak (2018)

Ian (2019)

Sub_region	2010	2011	2012	2014	2015	2016	2017	2018	2019	2020	2021
BB	4	14	3	6	30	36	30	35	24	24	37
UK	7	19	3	8	45	51	42	45	30	31	45
MK	15	51	9	11	55	66	55	56	44	44	65
LK	18	61	9	11	58	72	60	76	53	46	70